import re import os from typing import List from src.utils_display import AutoEvalColumn from src.auto_leaderboard.model_metadata_type import get_model_type from huggingface_hub import HfApi import huggingface_hub api = HfApi(token=os.environ.get("H4_TOKEN", None)) def get_model_infos_from_hub(leaderboard_data: List[dict]): for model_data in leaderboard_data: model_name = model_data["model_name_for_query"] try: model_info = api.model_info(model_name) except huggingface_hub.utils._errors.RepositoryNotFoundError: print("Repo not found!", model_name) model_data[AutoEvalColumn.license.name] = None model_data[AutoEvalColumn.likes.name] = None model_data[AutoEvalColumn.params.name] = get_model_size(model_name, None) continue model_data[AutoEvalColumn.license.name] = get_model_license(model_info) model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info) model_data[AutoEvalColumn.params.name] = get_model_size(model_name, model_info) def get_model_license(model_info): try: return model_info.cardData["license"] except Exception: return None def get_model_likes(model_info): return model_info.likes size_pattern = re.compile(r"\d+(b|m)") def get_model_size(model_name, model_info): # In billions try: return round(model_info.safetensors["total"] / 1e9, 3) except AttributeError: try: size_match = re.search(size_pattern, model_name.lower()) size = size_match.group(0) return round(int(size[:-1]) if size[-1] == "b" else int(size[:-1]) / 1e3, 3) except AttributeError: return None def apply_metadata(leaderboard_data: List[dict]): get_model_type(leaderboard_data) get_model_infos_from_hub(leaderboard_data)